Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/marcusrbrown/systematic/agent-native-architecturenpx skills add marcusrbrown/systematic --skill agent-native-architecturegit clone --depth 1 https://github.com/marcusrbrown/systematicWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/marcusrbrown/systematic/agent-native-architecture)<a href="https://agentmods.dev/skills/marcusrbrown/systematic/agent-native-architecture"><img src="https://agentmods.dev/badge/skills/marcusrbrown/systematic/agent-native-architecture.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00047 | $0.04843 |
| Opus 5 | $0.00023 | $0.02422 |
| Sonnet 5 | $0.00009 | $0.00969 |
| Haiku 4.5 | $0.00005 | $0.00484 |
Grade A, and why
agent-native-architecture scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
89% identical to agent-native-architecture — 63 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 437 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<why_now>
Why Now
Software agents work reliably now. OpenCode demonstrated that an LLM with access to bash and file tools, operating in a loop until an objective is achieved, can accomplish complex multi-step tasks autonomously.
The surprising discovery: a really good coding agent is actually a really good general-purpose agent. The same architecture that lets OpenCode refactor a codebase can let an agent organize your files, manage your reading list, or automate your workflows.
The OpenCode SDK makes this accessible. You can build applications where features aren't code you write—they're outcomes you describe, achieved by an agent with tools, operating in a loop until the outcome is reached.
This opens up a new field: software that works the way OpenCode works, applied to categories far beyond coding. </why_now>
<core_principles>
Core Principles
1. Parity
Whatever the user can do through the UI, the agent should be able to achieve through tools.
This is the foundational principle. Without it, nothing else matters.
Imagine you build a notes app with a beautiful interface for creating, organizing, and tagging notes. A user asks the agent: "Create a note summarizing my meeting and tag it as urgent."
If you built UI for creating notes but no agent capability to do the same, the agent is stuck. It might apologize or ask clarifying questions, but it can't help—even though the action is trivial for a human using the interface.
The fix: Ensure the agent has tools (or combinations of tools) that can accomplish anything the UI can do.
This isn't about creating a 1:1 mapping of UI buttons to tools. It's about ensuring the agent can achieve the same outcomes. Sometimes that's a single tool (create_note). Sometimes it's composing primitives (write_file to a notes directory with proper formatting).
The discipline: When adding any UI capability, ask: can the agent achieve this outcome? If not, add the necessary tools or primitives.
What ships with it
14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/action-parity-discipline.md 11 KB
- references/agent-execution-patterns.md 13 KB
- references/agent-native-testing.md 16 KB
- references/architecture-patterns.md 17 KB
- references/dynamic-context-injection.md 9.4 KB
- references/files-universal-interface.md 9.9 KB
- references/from-primitives-to-domain-tools.md 12 KB
- references/mcp-tool-design.md 15 KB
- references/mobile-patterns.md 25 KB
- references/product-implications.md 13 KB
- references/refactoring-to-prompt-native.md 8.4 KB
- references/self-modification.md 7.7 KB
- references/shared-workspace-architecture.md 20 KB
- references/system-prompt-design.md 6.4 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 437 lines · 47 tokens per session scan A b2e83cc11d0f
agent-native-architecture is a skill published in the GitHub repository marcusrbrown/systematic (24 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 4,843 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to agent-native-architecture, differing in 63 lines, and is treated as a copy.
Other skills, from other repositories
plan-protocol
Guidelines for creating and managing implementation plans with citations.
plan-review
Criteria for reviewing implementation plans against quality standards.
code-review
Comprehensive code review methodology with severity classification and confidence thresholds.
code-philosophy
Internal logic and data flow philosophy (The 5 Laws of Elegant Defense). Understand deeply to ensure code guides data naturally and prevents errors.
frontend-philosophy
Visual & UI philosophy (The 5 Pillars of Intentional UI). Understand deeply to avoid "AI slop" and create distinctive, memorable interfaces.
opencode-ensemble
Use when coordinating multiple coding agents, delegating independent software work, managing OpenCode Ensemble teams, choosing teammate roles or models, reviewing teammate output, or deciding whether parallel execution is appropriate.